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Preventing disease progression in schizophrenia: What are we waiting for.
J Psychiatr Res
December 2024
Department of Psychiatry, Stellenbosch University, Cape Town, South Africa.
Despite research advances and progress in health care, schizophrenia remains a debilitating and costly disease. Onset occurs typically during youth and can lead to a relapsing and ultimately chronic course with persistent symptoms and functional impairment if not promptly and properly treated. Consequently, over time, schizophrenia causes substantial distress and disability for patients, their families and accrues to a collective burden to society.
View Article and Find Full Text PDFThe role of large language models in self-care: a study and benchmark on medicines and supplement guidance accuracy.
Int J Clin Pharm
December 2024
Laboratory of Physiopharmacology, University of Antwerp, Universiteitsplein 1, 2610, Antwerp, Belgium.
Background: The recent surge in the capabilities of artificial intelligence systems, particularly large language models, is also impacting the medical and pharmaceutical field in a major way. Beyond specialized uses in diagnostics and data discovery, these tools have now become accessible to the general public.
Aim: The study aimed to critically analyse the current performance of large language models in answering patient's self-care questions regarding medications and supplements.
Reliability and quality of information provided by artificial intelligence chatbots on post-contrast acute kidney injury: an evaluation of diagnostic, preventive, and treatment guidance.
Rev Assoc Med Bras (1992)
December 2024
Bursa City Hospital, Department of Radiology - Bursa, Türkiye.
Objective: The aim of this study was to evaluate the reliability and quality of information provided by artificial intelligence chatbots regarding the diagnosis, preventive methods, and treatment of contrast-associated acute kidney injury, while also discussing their benefits and drawbacks.
Methods: The most frequently asked questions regarding contrast-associated acute kidney injury on Google Trends between January 2022 and January 2024 were posed to four artificial intelligence chatbots: ChatGPT, Gemini, Copilot, and Perplexity. The responses were evaluated based on the DISCERN score, the Patient Education Materials Assessment Tool for Printable Materials score, the Web Resource Rating scale, the Coleman-Liau index, and a Likert scale.
Large language models' performances regarding common patient questions about osteoarthritis: A comparative analysis of ChatGPT-3.5, ChatGPT-4.0, and perplexity.
J Sport Health Sci
November 2024
Department of Orthopaedics and Traumatology, Faculty of Medicine, The Chinese University of Hong Kong, 999077 Hong Kong, China; Center for Neuromusculoskeletal Restorative Medicine (CNRM), The Chinese University of Hong Kong, 999077 Hong Kong, China. Electronic address:
Background: Large Language Models (LLMs) have gained much attention and, in part, have replaced common search engines as a popular channel for obtaining information due to their contextually relevant responses. Osteoarthritis (OA) is a common topic in skeletal muscle disorders, and patients often seek information about it online. Our study evaluated the ability of 3 LLMs (ChatGPT-3.
View Article and Find Full Text PDFArtificial intelligence versus clinical judgement: how accurately do generative models reflect CNS guidelines for chiari malformation?
Clin Neurol Neurosurg
January 2025
Departments of Orthopaedic Surgery and Neurological Surgery, Duke University Health System, Durham, NC, USA. Electronic address:
Article Synopsis
- The study evaluated how well four generative AI models (Perplexity, ChatGPT 4o, Microsoft Copilot, and Google Gemini) answered questions based on the 2023 CNS guidelines for Chiari 1 malformation, categorizing their responses as "concordant" or "non-concordant."
- Perplexity had the highest concordance rate at 69.2%, while ChatGPT had the lowest at 23.1%. All AI models had issues with over-conclusive answers and varying degrees of insufficient responses.
- Readability tests indicated all models produced complex and challenging responses, highlighting the need for caution in using AI for clinical decision-making due to their low alignment with established medical
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